System and method for peer group detection, visualization and analysis in identity management artificial intelligence systems using cluster based analysis of network identity graphs

ABSTRACT

Systems and methods for graph based artificial intelligence systems for identity management systems are disclosed. Embodiments of the identity management systems disclosed herein may utilize a network graph approach to peer grouping of identities of distributed networked enterprise computing environment. Specifically, in certain embodiments, data on the identities and the respective entitlements assigned to each identity as utilized in an enterprise computer environment may be obtained by an identity management system. A network identity graph may be constructed using the identity and entitlement data. The identity graph can then be clustered into peer groups of identities. The peer groups of identities may be used by the identity management system and users thereof in risk assessment or other identity management tasks.

RELATED APPLICATIONS

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. 120 of the filing date of U.S. patent application Ser.No. 16/998,702 filed Aug. 20, 2020, issued as U.S. Pat. No. 11,196,804,entitled “SYSTEM AND METHOD FOR PEER GROUP DETECTION, VISUALIZATION ANDANALYSIS IN IDENTITY MANAGEMENT ARTIFICIAL INTELLIGENCE SYSTEMS USINGCLUSTER BASED ANALYSIS OF NETWORK IDENTITY GRAPHS”, which is acontinuation of, and claims a benefit of priority under 35 U.S.C. 120 ofthe filing date of U.S. patent application Ser. No. 16/582,493 filedSep. 25, 2019, issued as U.S. Pat. No. 10,791,170, entitled “SYSTEM ANDMETHOD FOR PEER GROUP DETECTION, VISUALIZATION AND ANALYSIS IN IDENTITYMANAGEMENT ARTIFICIAL INTELLIGENCE SYSTEMS USING CLUSTER BASED ANALYSISOF NETWORK IDENTITY GRAPHS”, which is a continuation of, and claims abenefit of priority under 35 U.S.C. 120 of the filing date of U.S.patent application Ser. No. 16/459,104 filed Jul. 1, 2019, issued asU.S. Pat. No. 10,476,953, entitled “SYSTEM AND METHOD FOR PEER GROUPDETECTION, VISUALIZATION AND ANALYSIS IN IDENTITY MANAGEMENT ARTIFICIALINTELLIGENCE SYSTEMS USING CLUSTER BASED ANALYSIS OF NETWORK IDENTITYGRAPHS”, which is a continuation of, and claims a benefit of priorityunder 35 U.S.C. 120 of the filing date of U.S. patent application Ser.No. 16/417,803 filed May 21, 2019, issued as U.S. Pat. No. 10,476,952,entitled “SYSTEM AND METHOD FOR PEER GROUP DETECTION, VISUALIZATION ANDANALYSIS IN IDENTITY MANAGEMENT ARTIFICIAL INTELLIGENCE SYSTEMS USINGCLUSTER BASED ANALYSIS OF NETWORK IDENTITY GRAPHS”, which is acontinuation of, and claims a benefit of priority under 35 U.S.C. 120 ofthe filing date of U.S. patent application Ser. No. 16/201,604 filedNov. 27, 2018, issued as U.S. Pat. No. 10,341,430, entitled “SYSTEM ANDMETHOD FOR PEER GROUP DETECTION, VISUALIZATION AND ANALYSIS IN IDENTITYMANAGEMENT ARTIFICIAL INTELLIGENCE SYSTEMS USING CLUSTER BASED ANALYSISOF NETWORK IDENTITY GRAPHS”, which are fully incorporated herein byreference for all purposes.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material towhich a claim for copyright is made. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but reserves all other copyright rightswhatsoever.

TECHNICAL FIELD

This disclosure relates generally to computer security. In particular,this disclosure relates to identity management in a distributed andnetworked computing environment. Even more specifically, this disclosurerelates to graph based identity peer grouping and analysis, and uses ofthe same for identity governance and management in an enterprisecomputing environment.

BACKGROUND

Acts of fraud, data tampering, privacy breaches, theft of intellectualproperty, and exposure of trade secrets have become front page news intoday's business world. The security access risk posed byinsiders—persons who are granted access to information assets—is growingin magnitude, with the power to damage brand reputation, lower profits,and erode market capitalization.

Identity Management (IM), also known as Identity and Access Management(IAM) or Identity Governance (IG), is, the field of computer securityconcerned with the enablement and enforcement of policies and measureswhich allow and ensure that the right individuals access the rightresources at the right times and for the right reasons. It addresses theneed to ensure appropriate access to resources across increasinglyheterogeneous technology environments and to meet increasingly rigorouscompliance requirements. Escalating security and privacy concerns aredriving governance, access risk management, and compliance to theforefront of identity management. To effectively meet the requirementsand desires imposed upon enterprises for identity management, theseenterprises may be required to prove that they have strong andconsistent controls over who has access to critical applications anddata. And, in response to regulatory requirements and the growingsecurity access risk, most enterprises have implemented some form ofuser access or identity governance.

Yet many companies still struggle with how to focus compliance effortsto address actual risk in what usually is a complex, distributednetworked computing environment. Decisions about which accessentitlements are desirable to grant a particular user are typicallybased on the roles that the user plays within the organization. In largeorganizations, granting and maintaining user access entitlements is adifficult and complex process, involving decisions regarding whether togrant entitlements to thousands of users and hundreds of differentapplications and databases. This complexity can be exacerbated by highemployee turnover, reorganizations, and reconfigurations of the variousaccessible systems and resources.

Organizations that are unable to focus their identity compliance effortson areas of greatest access risk can waste time, labor, and otherresources applying compliance monitoring and controls across the boardto all users and all applications. Furthermore, with no means toestablish a baseline measurement of identity compliance, organizationshave no way to quantify improvements over time and demonstrate thattheir identity controls are working and effectively reducing accessrisk.

Information Technology (IT) personnel of large organizations often feelthat their greatest security risks stemmed from “insider threats,” asopposed to external attacks. The access risks posed by insiders rangefrom careless negligence to more serious cases of financial fraud,corporate espionage, or malicious sabotage of systems and data.Organizations that fail to proactively manage user access can faceregulatory fines, litigation penalties, public relations fees, loss ofcustomer trust, and ultimately lost revenue and lower stock valuation.To minimize the security risk posed by insiders (and outsiders),business entities and institutions alike often establish access or othergovernance policies that eliminate or at least reduce such access risksand implement proactive oversight and management of user accessentitlements to ensure compliance with defined policies and other goodpractices.

To assist in mitigating these risks, therefore, it is of utmostimportance to effectively analyze access or entitlement data in theenterprise environment to determine or assess the efficacy orenforcement of such governance policies and to identify potential risks.Consequently, what is desired are improved ways to quantitatively orqualitatively analyze access data in distributed networked computingenvironment and to utilize the results of such analysis to improveidentity governance in that environment.

SUMMARY

Accordingly, to ameliorate or address these issues, among other ends,embodiments of the identity management systems disclosed herein mayutilize a network graph approach to peer grouping of identities ofdistributed networked enterprise computing environment. Specifically, incertain embodiments, data on the identities and the respectiveentitlements assigned to each identity as utilized in an enterprisecomputer environment may be obtained by an identity management system.Using the identity and entitlement data, then, a network identity graphmay be constructed, where the nodes of the graph correspond to, andrepresent, each of the identities. Each edge (or relationship) of thegraph may join two nodes of the graph and be associated with asimilarity weight representing a degree of similarity between theidentities of the respective nodes. The identity graph may then bepruned to remove weak edges (e.g., those edges whose similarity weightmay fall below a pruning threshold). The pruned identity graph can thenbe clustered into peer groups of identities (e.g., using a graph basedcommunity detection algorithm). These peer groups of identities can thenbe stored (e.g., separately or in the identity graph) and used by theidentity management system. For example, a visual representation of thegraph may be presented to a user of the identity management to assist incompliance or certification assessments or evaluation of the identitiesand entitlements as currently used by the enterprise.

In certain embodiments, the clustering of identities may be optimizedbased on a peer group assessment metric, such as, for example, graphmodularity determined based on the identity graph or the determined peergroups. For instance, in one embodiment if a peer group assessmentmetric is below (or above) a quality threshold a feedback loop may beinstituted whereby the pruning threshold is adjusted by some amount (upor down) and the originally determined identity graph is pruned based onthe adjusted pruning threshold (or the previously pruned identity graphmay be further pruned). This newly pruned identity graph can then beclustered into new peer groups of identities and a peer group assessmentmetric determined based on the newly pruned identity graph or the newlydetermined peer groups. If this new peer assessment metric is now above(or below) the quality threshold the feedback loop may stop and thesepeer groups of identities can then be stored (e.g., separately or in theidentity graph) and used by the identity management system.

Otherwise, the feedback loop may continue by again adjusting the pruningthreshold further (e.g., further up or further down relative to theprevious iteration of the feedback loop), re-pruning the identity graphbased on the adjusted pruning threshold, clustering this newly prunedgraph, determining another peer group assessment metric and comparingthis metric to the quality threshold. In this manner, the feedback loopof adjustment of the pruning threshold, re-pruning the graph,re-clustering the identity graph into peer groups may be repeated untilthe peer group assessment metric reaches a desired threshold. Moreover,by tailoring the peer group assessment metric and quality threshold toinclude or reflect domain or enterprise specific criteria, theclustering results (e.g., the peer groups resulting from the clustering)may more accurately reflect particular requirements or the needs of aparticular enterprise or be better tailored to a particular use.

Embodiments provide numerous advantages over previously availablesystems and methods for measuring access risk. As embodiments are basedon a graph representation of identity management data, the graphstructure may serve as a physical model of the data, allowing moreintuitive access to the data (e.g., via graph database querying, or viagraph visualization techniques). This ability may yield deeper and morerelevant insights for users of identity management systems. Suchabilities are also an outgrowth of the accuracy of the results producedby embodiments as disclosed.

Moreover, embodiments as disclosed may offer the technologicalimprovement of reducing the computational burden and memory requirementsof systems implementing these embodiments through the improved datastructures and the graph processing and analysis implemented by suchembodiments. Accordingly, embodiments may improve the performance andresponsiveness of identity management systems that utilize suchembodiments of identity graphs and clustering approaches by reducing thecomputation time and processor cycles required (e.g., and thus improvingprocessing speed) and simultaneously reducing memory usage or othermemory requirements.

Similarly, a network graph approach to peer grouping will expose andutilize the strong homophily aspects inherent in this use case. Bycapturing the homophilic nature of identity governance, the opportunityarises for a large number of applications of the peer groups an identitygraphs, including, for example, identification and mitigation of outlieridentities, role mining, automation of access approval and certificationcampaigns, predictive modeling of entitlement spread or diffusion withina peer group or the whole population and compliance assessment usecases, among others.

As yet another advantage, embodiments may be dynamic with respect totime, allowing the development update processes using deltas betweensnapshots of data collection, bringing down operational costs andimproving the performance and robustness of embodiments.

Moreover, the graph format used by certain embodiments, allows thetranslation of domain and enterprise specific concepts, phenomena, andissues into tangible, quantifiable, and verifiable hypotheses which maybe examined or validated with graph-based algorithms. Accordingly,embodiments may be especially useful in assessing risk and in compliancewith security policies or the like.

Historically, such security risks associated with user entitlements havebeen hard to quantify. In large organizations, user access data or dataon user entitlements can be scattered across hundreds of systems andapplications and can be difficult to compile, analyze, and present in amanageable format to the persons in position to act on the information.Consequently, most organizations attempt to manage risk in adecentralized manner, focusing on a single application or system at atime.

Such decentralized, one-at-a-time approaches have several drawbacks.With such approaches, managers, auditors or compliance officers may notgain enterprise level visibility of access risk across all at-riskresources. Risk management, even within an organization, may be appliedsporadically and thus may prove to be insufficient or ineffective inminimizing access risks posed by inside users. Also, when riskmanagement is decentralized, baselines (such as standards, measures,benchmarks, etc.) utilized in assessing risk may vary from department todepartment, system to system, and application to application even withinthe same organization. Moreover, previously available approaches can betime consuming, tedious, impracticable, and expensive since conventionalrisk management processes often consist of manual reviews of userentitlements and access lists.

Systems and methods disclosed herein can provide IT compliance andgovernance managers, auditors, compliance officers and others simple,intuitive means to assess the effectiveness of identity management andthe associated access risk across large numbers of identities,entitlements, users, applications, systems, etc. By increasing thevisibility of user access risk at various levels across variousresources, enterprises can pinpoint at-risk areas and focus theirsecurity and access control efforts where such focus may be desired.

Various embodiments may thus allow for new, in-depth insights intoaccess risk which can enable enterprises to efficiently, effectively,and globally track, analyze, and control user access to resources.Access risks can be quickly and easily assessed in some embodiments.Access risk issues can be identified, prioritized, and immediatelyremediated or mitigated in various embodiments. Access risk management,in accordance with various embodiments, can help ensure regulatorycompliance in a cost effective manner while also meeting appropriatestandards related to enterprise governance. In accordance with someembodiments, organizations can focus their access risk managementefforts strategically, track progress over time, and providequantifiable proof of enhanced security and reduced access risk.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousembodiments of the disclosure and numerous specific details thereof, isgiven by way of illustration and not of limitation. Many substitutions,modifications, additions and/or rearrangements may be made within thescope of the disclosure without departing from the spirit thereof, andthe disclosure includes all such substitutions, modifications, additionsand/or rearrangements.

BRIEF DESCRIPTION OF THE FIGURES

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the invention. A clearerimpression of the invention, and of the components and operation ofsystems provided with the invention, will become more readily apparentby referring to the exemplary, and therefore nonlimiting, embodimentsillustrated in the drawings, wherein identical reference numeralsdesignate the same components. Note that the features illustrated in thedrawings are not necessarily drawn to scale.

FIG. 1 is a block diagram of a distributed networked computerenvironment including one embodiment of an identity management system.

FIG. 2 is a flow diagram of one embodiment of a method for peer groupdetection and analysis using cluster based analysis of identity graphs.

FIGS. 3A, 3B, 3C and 3D depict example visual representations ofidentity graphs.

FIGS. 4-7 depict interfaces that may be utilized by embodiments of anidentity management system.

DETAILED DESCRIPTION

The invention and the various features and advantageous details thereofare explained more fully with reference to the non-limiting embodimentsthat are illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well-known starting materials,processing techniques, components and equipment are omitted so as not tounnecessarily obscure the invention in detail. It should be understood,however, that the detailed description and the specific examples, whileindicating some embodiments of the invention, are given by way ofillustration only and not by way of limitation. Various substitutions,modifications, additions and/or rearrangements within the spirit and/orscope of the underlying inventive concept will become apparent to thoseskilled in the art from this disclosure.

Before delving into more detail regarding the specific embodimentsdisclosed herein, some context may be helpful. In response to regulatoryrequirements and security access risks and concerns, most enterpriseshave implemented some form of computer security or access controls. Toassist in implementing security measures and access controls in anenterprise environment, many of these enterprises have implementedIdentity Management in association with their distributed networkedcomputer environments. Identity Management solutions allow thedefinition of a function or an entity associated with an enterprise. Anidentity may thus be almost physical or virtual thing, place, person orother item that an enterprise would like to define. Identities cantherefore be, for example, roles or capacities (e.g., manager, engineer,team leader, etc.), title (e.g., Chief Technology Officer), groups(development, testing, accounting, etc.), processes (e.g., nightlyback-up process), physical locations (e.g., cafeteria, conference room),individual users or humans (e.g., John Locke) or almost any otherphysical or virtual thing, place, person or other item. Each of theseidentities may therefore be assigned zero or more entitlements withrespect to the distributed networked computer environments. Anentitlement may be the ability to perform or access a function withinthe distributed networked computer environments, including, for example,accessing computing systems, applications, file systems, particular dataor data items, networks, subnetworks or network locations, etc.

By managing the identity or identities to which users within theenterprise computing environment are assigned, the entitlements which auser may assigned (e.g., the functions or access which a user may beallowed) may be controlled. However, escalating security and privacyconcerns are driving governance, access risk management, and complianceto the forefront of Identity Management. To effectively meet therequirements and desires imposed upon enterprises for IdentityManagement these enterprises may be required to prove that they havestrong and consistent controls over who has access to criticalapplications and data.

Yet many companies still struggle with how to focus compliance effortsto address actual risk in what usually is a complex, distributednetworked computing environment. Decisions about which accessentitlements are desirable to grant a particular user are typicallybased on the roles that the user plays within the organization. In largeorganizations, granting and maintaining user access entitlements is adifficult and complex process, involving decisions regarding whether togrant entitlements to thousands of users and hundreds of differentapplications and databases. This complexity can be exacerbated by highemployee turnover, reorganizations, and reconfigurations of the variousaccessible systems and resources.

Generally, however, good governance practice in the identity spacerelies on the ‘social’ principle that identities with strongly similarattributes should be assigned similar, if not identical, accessentitlements. In the realm of identity governance and administration,this approach allows for a separation of duties and thus makes itfeasible to identify, evaluate, and prioritize risks associated withprivileged access.

As part of a robust identity management system, it is therefore highlydesirable to analyze an enterprise's data to identify potential risks.In principle, strictly enforced pre-existing governance policies shouldensure that identities with strongly similar access privileges arestrongly similar. It would thus be desirable to group or cluster theidentities of an enterprise into peer groups such that the identities ina peer group are similar with respect to the set of entitlementsassigned to the identities of that group (e.g., relative to otheridentities or other groups). Peer grouping of the identities within anenterprise (or viewing the peer groups of identities) may allow, forexample, an auditor other person performing a compliance analysis orevaluation to quantitatively and qualitatively assess the effectivenessof any applicable pre-existing polices, or lack thereof, and howstrictly they are enforced.

However, the data utilized by most identity management systems is notstrictly numerical data. Often this data includes identifications ofidentities (e.g., alphanumeric identifiers for an identity as maintainedby an identity management system) and identifications of entitlementsassociated with those identities (e.g., alphanumeric identifiers forentitlements as maintained by the identity management system. Clusteringof this type of categorical data (e.g., for peer grouping of identities)is typically a harder task than clustering data of numerical type. Inparticular, clustering categorical data is particularly challengingsince intuitive, geometric-based, distance measures experienced in reallife, e.g., Euclidean distance, by definition, are exclusive tonumerical data. A distance measure is a crucial component of anyclustering algorithm as it is utilized at the lowest level to determinehow similar/dissimilar two data points are.

For example, the one-hot-encoding data transform, which can convertcategorical data into numerical data, does not work in these types ofcases. Due to large number of entitlements, when combining thenumerical, high-dimensional, one-hot encoded data with traditionalgeometric distances (e.g., Euclidean), distances between data pointswill be quite large and will make it hard, if not impossible, for aclustering algorithm to yield meaningful outputs. This is a directmathematical outcome to the high dimensionality of the ambient space. Itis a well-documented issue in data science literature, and theapplicable nomenclature is “curse of dimensionality”. Typicaldimensionality reduction techniques, e.g., PCA, t-SNE, have beenexperimented with, but due to the way these clustering algorithmsmanipulate numerical data, the resulting transforms may manipulate theoriginal data in ways that are not interpretable, hence not useful inthis context.

Accordingly, conventional statistical clustering such as K-modes, orK-modes used in association with a data-mining, pattern-findingalgorithm such as Equivalence Class Transformation (ECLAT), have thusproven inadequate. Many of the reasons for the inadequacy of suchtypical clustering approaches have to do with the computationallyintensive nature of the computer implementations of such clustering,which are both computationally and memory intensive, reducing orhindering the performance and responsiveness of identity managementsystems that utilize such clustering approaches.

Attempts to remedy these problems by altering the clustering to discardor ignore less popular identities or entitlements to enhance thesignal-to-noise ratio in their application have been less thansuccessful, achieving neither adequate results in the clustersdetermined or in improving the performance or memory usage of systemswhich employ such clustering. Other workarounds for these deficiencieshave also proven unworkable to this type of identity and entitlementdata.

Moreover, when attempting to cluster based on categorical data, typicalclustering algorithms do not capture the social aspects of identitygovernance. Homophily in social networks, as defined in social sciences,is the tendency of individuals to associate and bond with similarothers. In identity governance, homophily in the identity space usuallyresults as a consequence of enforcing the governance principle thatsimilar identities should be assigned similar access entitlements. It isthus important to attempt to capture, or otherwise utilize thishomophily, when peer grouping for identity management. As a consequenceof all these deficiencies, the results from prior approach to identityclustering in the context of identity management were harder tointerpret, yielding fewer insights, and negatively impacting the speed,efficiency, and overall quality of identity management systems. Thedata-driven clustering approach of identities into peer groups remains,however, a crucial component of identity management in a distributed andnetworked computing environment for a variety of reasons, including theusefulness of reviewing and visualizing such clusters of identities forauditing and compliance purposes.

Accordingly, to ameliorate these issues, among other ends, embodimentsof the identity management systems disclosed herein may utilize anetwork graph approach to peer grouping of identities of distributednetworked enterprise computing environment. Specifically, in certainembodiments, data on the identities and the respective entitlementsassigned to each identity as utilized in an enterprise computerenvironment may be obtained by an identity management system. Using theidentity and entitlement data, then, a network identity graph may beconstructed, where the nodes of the graph correspond to, and represent,each of the identities. Each edge (or relationship) of the graph mayjoin two nodes of the graph and be associated with a similarity weightrepresenting a degree of similarity between the identities of therespective nodes. The identity graph may then be pruned to remove weakedges (e.g., those edges whose similarity weight may fall below apruning threshold). The pruned identity graph can then be clustered intopeer groups of identities (e.g., using a graph based community detectionalgorithm). These peer groups of identities can then be stored (e.g.,separately or in the identity graph) and used by the identity managementsystem. For example, a visual representation of the graph may bepresented to a user of the identity management to assist in complianceor certification assessments or evaluation of the identities andentitlements as currently used by the enterprise.

In certain embodiments, the clustering of identities may be optimizedbased on a peer group assessment metric, such as, for example, graphmodularity determined based on the identity graph or the determined peergroups. For instance, in one embodiment if a peer group assessmentmetric is below (or above) a quality threshold a feedback loop may beinstituted whereby the pruning threshold is adjusted by some amount (upor down) and the originally determined identity graph is pruned based onthe adjusted pruning threshold (or the previously pruned identity graphmay be further pruned). This newly pruned identity graph can then beclustered into new peer groups of identities and a peer group assessmentmetric determined based on the newly pruned identity graph or the newlydetermined peer groups. If this new peer assessment metric is now above(or below) the quality threshold the feedback loop may stop and thesepeer groups of identities can then be stored (e.g., separately or in theidentity graph) and used by the identity management system.

Otherwise, the feedback loop may continue by again adjusting the pruningthreshold further (e.g., further up or further down relative to theprevious iteration of the feedback loop), re-pruning the identity graphbased on the adjusted pruning threshold, clustering this newly prunedgraph, determining another peer group assessment metric and comparingthis metric to the quality threshold. In this manner, the feedback loopof adjustment of the pruning threshold, re-pruning the graph,re-clustering the identity graph into peer groups may be repeated untilthe peer group assessment metric reaches a desired threshold. Moreover,by tailoring the peer group assessment metric and quality threshold toinclude or reflect domain or enterprise specific criteria, theclustering results (e.g., the peer groups resulting from the clustering)may more accurately reflect particular requirements or the needs of aparticular enterprise or be better tailored to a particular use.

Embodiments may thus provide a number of advantages including allowingmore intuitive access to the data (e.g., via graph database querying, orvia graph visualization techniques), which may, in turn, yield deeperand more relevant insights for users of identity management systems.Moreover, embodiments as disclosed may offer the technologicalimprovement of reducing the computational burden and memory requirementsof systems implementing these embodiments through the improved datastructures and the graph processing and analysis implemented by suchembodiments. Accordingly, embodiments may improve the performance andresponsiveness of identity management systems that utilize suchembodiments. Likewise, embodiments may be dynamic with respect to time,allowing the development update processes using deltas between snapshotsof data collection, bringing down operational costs and improving theperformance and robustness of embodiments. Moreover, the graph formatused by certain embodiments, allows the translation of domain andenterprise specific concepts, phenomena, and issues into tangible,quantifiable, and verifiable hypotheses which may be examine or validatewith graph based algorithms. Accordingly, embodiments may be especiallyuseful in assessing risk and in compliance with security policies or thelike.

Turning first to FIG. 1 , then, a distributed networked computerenvironment including one embodiment of an identity management system isdepicted. Here, the networked computer environment may include anenterprise computing environment 100. Enterprise environment 100includes a number of computing devices or applications that may becoupled over a computer network 102 or combination of computer networks,such as the Internet, an intranet, an internet, a Wide Area Network(WAN), a Local Area Network (LAN), a cellular network, a wireless orwired network, or another type of network. Enterprise environment 100may thus include a number of resources, various resource groups andusers associated with an enterprise (for purposes of this disclosure anyfor profit or non-profit entity or organization). Users may have variousroles, job functions, responsibilities, etc. to perform within variousprocesses or tasks associated with enterprise environment 100. Users caninclude employees, supervisors, managers, IT personnel, vendors,suppliers, customers, robotic or application based users, etc.associated with enterprise 100.

Users may access resources of the enterprise environment 100 to performfunctions associated with their jobs, obtain information aboutenterprise 100 and its products, services, and resources, enter ormanipulate information regarding the same, monitor activity inenterprise 100, order supplies and services for enterprise 100, manageinventory, generate financial analyses and reports, or generally toperform any task, activity or process related to the enterprise 100.Thus, to accomplish their responsibilities, users may have entitlementsto access resources of the enterprise environment 100. Theseentitlements may give rise to risk of negligent or malicious use ofresources.

Specifically, to accomplish different functions, different users mayhave differing access entitlements to differing resources. Some accessentitlements may allow particular users to obtain, enter, manipulate,etc. information in resources which may be relatively innocuous. Someaccess entitlements may allow particular users to manipulate informationin resources of the enterprise 100 which might be relatively sensitive.Some sensitive information can include human resource files, financialrecords, marketing plans, intellectual property files, etc. Access tosensitive information can allow negligent or malicious activities toharm the enterprise itself. Access risks can thus result from a userhaving entitlements with which the user can access resources that theparticular user should not have access to; gain access to another user'sentitlements or for other reasons. Access risks can also arise fromroles in enterprise environment 100 which may shift, change, evolve,etc. leaving entitlements non optimally distributed among various users.

To assist in managing the entitlements assigned to various users andmore generally in managing and assessing access risks in enterpriseenvironment 100, an identity management system 150 may be employed. Suchan identity management system 150 may allow an administrative or othertype of user to define one or more identities and one or moreentitlements and associate these identities with entitlements using, forexample, an administrator interface 152. Examples of such identitymanagement systems are Sailpoint's IdentityIQ and IdentityNow products.Note here, that while the identity management system 150 has beendepicted in the diagram as separate and distinct from the enterpriseenvironment 100 and coupled to enterprise environment 100 over acomputer network 104 (which may the same as, or different than, network102), it will be realized that such an identity management system 150may be deployed as part of the enterprise environment 100, remotely fromthe enterprise environment, as a cloud based application or set ofservices, or in another configuration.

An identity may thus be almost physical or virtual thing, place, personor other item that an enterprise would like to define. For example, anidentity may be a role or capacity, title, groups, processes, physicallocations, individual users or humans or almost any other physical orvirtual thing, place, person or other item. An entitlement may be theability to perform or access a function within the distributed networkedenterprise computer environment 100, including, for example, accessingcomputing systems, applications, file systems, particular data or dataitems, networks, subnetworks or network locations, etc. Each of theseidentities may therefore be assigned zero or more entitlements withrespect to the distributed networked computer environments.

The identity management system 150 may thus store identity managementdata 154. The identity management data 154 stored may include a setentries, each entry corresponding to and including an identity (e.g.,alphanumerical identifiers for identities) as defined and managed by theidentity management system, a list or vector of entitlements assigned tothat identity by the identity management system, and a time stamp atwhich the identity management data was collected from the identitymanagement system. Other data could also be associated with eachidentity, including data that may be provided from other systems such asa title, location or department associated with the identity.

Collectors 156 of the identity management system 150 may thus request orotherwise obtain data from various touchpoint systems within enterpriseenvironment 100. These touchpoint systems may include, for exampleActive Directory systems, Java Database Connectors within the enterprise100, Microsoft SQL servers, Azure Active Directory servers, OpenLDAPservers, Oracle Databases, SalesForce applications, ServiceNowapplications, SAP applications or Google GSuite.

Accordingly, the collectors 156 of the identity management system 150may obtain or collect event data from various systems within theenterprise environment 100 and process the event data to associate theevent data with the identities defined in the identity management data154 to evaluate or analyze these events or other data in an identitymanagement context. A user may interact with the identity managementsystem 150 through a user interface 158 to access or manipulate data onidentities, entitlements, events or generally identity management withrespect to enterprise environment 100.

As part of a robust identity management system, it is desirable toanalyze the identity management data 154 associated with an enterprise100. Specifically, It is desirable to group or cluster the identities ofan enterprise 100 into peer groups such that the identities in a peergroup are similar with respect to the set of entitlements assigned tothe identities of that group (e.g., relative to other identities orother groups). Peer grouping of the identities within an enterprise (orviewing the peer groups of identities) may allow, for example, anauditor other person performing a compliance analysis or evaluation toquantitatively and qualitatively assess the effectiveness of anyapplicable pre-existing polices, or lack thereof, and how strictly theyare enforced.

Accordingly, an identity management system 160 may include a harvester162 and a graph generator 164. The harvester 162 may obtain identitymanagement data from one or more identity management systems 150associated with enterprise 100. The identity management data may beobtained, for example, as part of a regular collection or harvestingprocess performed at some regular interval by connecting to, andrequesting the identity management data from, the identity managementsystem 150. The identity management data stored may thus include a setentries, each entry corresponding to and including an identity asdefined and managed by the identity management system, a list or vectorof entitlements assigned to that identity by the identity managementsystem, and a time stamp at which the identity management data wascollected from the identity management system 150.

Graph generator 164 may generate a peer grouped identity graph from theobtained identity management data. Specifically, in one embodiment, anidentity graph may be generated from the identity management dataobtained from the enterprise. Each of the identities from the mostrecently obtained identity management data and a node of the graphcreated for each identity. An edge is constructed between every pair ofnodes (e.g., identities) that shares at least one entitlement. Each edgeof the graph may also be associated with a similarity weightrepresenting a degree of similarity between the identities of therespective nodes joined by that edge. Accordingly, the obtained identitymanagement data may be represented by an identity graph and stored ingraph data store 166.

Once the identity graph is generated by the graph generator 164, thegraph may then be pruned to remove edges based on their weighting. Thepruned identity graph can then be used to cluster the identities intopeer groups of identities. This clustering may be accomplished, forexample, a community-detection algorithm. This clustering result mayalso be optimized by the graph generator 164 through the use of afeedback loop to optimize the pruning of the edges until a desiredmetric for assessing the quality of the peer groups generated exceeds adesired threshold. Once the peer groups of identities are determined,the peer groups can then be stored (e.g., separately or in the identitygraph itself) and used by the identity management system 160. Forexample, each peer group may be assigned a peer group identifier and thepeer group identifier associated with each identity assigned to the peergroup by storing the peer group identifier in association with the nodein the graph representing that identity.

An interface 168 of the identity management system 160 may use theidentity graph in the graph data store 166 or associated peer groups topresent one or more interface which may be used for risk assessment, aswill be discussed. For example, an interface 168 may present a visualrepresentation of the graph, the identities or the peer groups in theidentity graph to a user of the identity management system 160associated with enterprise 100 to assist in compliance or certificationassessments or evaluation of the identities and entitlements ascurrently used by the enterprise (e.g., as represented in identitymanagement data 154 of identity management system 150).

Before moving on, it will be noted here that while identity managementsystem 160 and identity management system 150 have been depictedseparately for purposes of explanation and illustration, it will beapparent that the functionality of identity management systems 150, 160may be combined into a single or a plurality of identity managementsystem as is desired for a particular embodiment and the depiction andseparation of the identity management systems and their respectivefunctionality has been depicted separately solely for purposes of easeof depiction and description.

Turning now to FIG. 2 , a flow diagram for one embodiment of a methodfor determining peer groups of identities using a graph database isdepicted. Embodiments of such a method may be employed by graphgenerators of identity management systems to generate identity graphsand associated peer groups from identity management data, as discussedabove. Initially, at step 210, identity management data may be obtained.As discussed, in one embodiment, this identity management data may beobtained from one or more identity management systems that are deployedin association with an enterprise's distributed computing environment.Thus, the identity management data may be obtained, for example, as partof a regular collection or harvesting process performed at some regularinterval by connecting to, requesting the identity management data from,an identity management system. The identity management data may also beobtained on a one-time or user initiated basis.

As will be understood, the gathering of identity management data anddetermination of peer groups can be implemented on a regular,semi-regular or repeated basis, and thus may be implemented dynamicallyin time. Accordingly, as the data is obtained, it may be stored as atime-stamped snapshot. The identity management data stored may thusinclude a set entries, each entry corresponding to and including anidentity (e.g., alphanumerical identifiers for identities) as definedand managed by the identity management system, a list or vector ofentitlements assigned to that identity by the identity managementsystem, and a time stamp at which the identity management data wascollected from the identity management system. Other data could also beassociated with each identity, including data that may be provided froman identity management system such as a title, location or departmentassociated with the identity. The collection of entries or identitiesassociated with the same timestamp can thus be thought of as a snapshotfrom that time of the identities and entitlements of the enterprisecomputing environment as management by the identity management system.

As an example of identity management data that may be obtained from anidentity management system, the following is one example of a JavascriptObject Notation (JSON) object that may relate to an identity:

{ ″attributes″: { ″Department″: ″Finance″, ″costcenter″: ″[R01e, L03]″,″displayName″: ″Catherine Simmons″, ″email″:″Catherine.Simmons@demoexample.com″, ″empld″: ″1b2c3d″, ″firstname″:″Catherine″, ″inactive″: ″false″, ″jobtitle″: ″Treasury Analyst″,″lastname″: ″Simmons″, ″location″: ″London″, ″manager″: ″Amanda.Ross″,″region″: ″Europe″, ″riskScore″: 528, ″startDate″: ″12/31/201600:00:00AM UTC″, ″nativeldentity_source_2″: ″source_2″,″awesome_attribute_source_1″: ″source_1″, ″twin_attribute_a″: ″twin a″,″twin_attribute_b″: ″twin b″, ″twin_attribute_c″: ″twin c″ }, ″id″:″2c9084ee5a8de328015a8de370100082″, ″integration_id″: ″iiq″,″customer_id″: ″ida-bali″, ″meta″: {  ″created″:″2017-03-02T07:19:37.233Z″,  ″modified″: ″2017-03-02T07:24:12.024Z″ }, ″name″: ″Catherine.Simmons″,  ″refs″: {   ″accounts″: {    ″id″: [    ″2c9084ee5a8de328015a8de370110083″    ],    ″type″: ″account″   },  ″entitlements″: {    ″id″: [     ″2c9084ee5a8de328015a8de449060e54″,    ″2c9084ee5a8de328015a8de449060e55″    ]    ″type″: ″entitlement″  },   ″manager″: {    ″id″: [     ″2c9084ee5a8de022015a8de0c52b031d″   ]    ″type″: ″identity″   }  },  ″type″: ″identity″ }

As another example of identity management data that may be obtained froman identity management system, the following is one example of a JSONobject that may relate to an entitlement:

{  ″integration_id″: ″bd992e37-bbe7-45ae-bbbf-c97a59194cbc″,  ″refs″: {  ″application″: {    ″id″: [     ″2c948083616ca13a01616ca1d4aa0301″   ],    ″type″: ″application″   }  },  ″meta″: {   ″created″:″2018-02-06T19:40:08.005Z″,   ″modified″: ″2018-02-06T19:40:08.018Z″  }, ″name″: ″Domain Administrators″,  ″attributes″: {   ″description″:″Domain Administrators group on Active Directory″,   ″attribute″:″memberOf″,   ″aggregated″: true,   ″requestable″: true,   ″type″:″group″,   ″value″: ″cn=Domain Administrators,dc=domain,dc=local″  }, ″id″: ″2c948083616ca13a01616ca1f1c50377″,  ″type″: ″entitlement″, ″customer_id″: ″3a60b474-4f43-4523-83d1-eb0fd571828f″ }

At step 220 an identity graph may be generated from the identitymanagement data obtained from the enterprise. Specifically, each of theidentities from the most recent snapshot of identity management data maybe obtained and a node of the graph created for each identity. An edgeis constructed between every pair of nodes (e.g., identities) thatshares at least one entitlement. (e.g., an edge connects two identitynodes if and only if they have at least one entitlement in common). Eachedge of the graph may be associated with a similarity weightrepresenting a degree of similarity between the identities of therespective nodes joined by that edge. This similarity weight may begenerated based on the number of entitlements shared between the twojoined nodes. As but one example, the similarity weight could be basedon a count of the similarity (e.g., overlap or intersection ofentitlements) between the two identities divided by the union ofentitlements. For example, in one embodiment, the edges are weighted viaa proper similarity function (e.g., Jaccard similarity). In oneembodiment, a dissimilarity measure, of entitlement binary vectors, d,may be chosen, then the induced similarity, 1-d(x,y), may be used toassign a similarity weight to the edge joining the nodes, x,y. Othermethods for determining a similarity weight between two nodes arepossible and are fully contemplated herein.

In one specific, embodiment, a symmetric matrix may be determined witheach of the user identities along each axis of the matrix. The diagonalof the matrix may be all 0s while the rest of values are the similarityweights determined between the two nodes on the axes corresponding tothe value. In this manner, this symmetric matrix may be provided to agraph constructor which translates the identities on the axes and thesimilarity values of the matrix into graph store commands to constructthe identity graph.

Accordingly, the identity management data may be faithfully representedby a k-partite graph, with k types of entities (nodes/vertices, e.g.,identity-id, title, location, entitlement, etc.) and stored in a graphdata store. It will be noted that graph data store 132 may be stored inany suitable format and according to any suitable storage, including,for example, a graph store such a Neo4j, a triple store, a relationaldatabase, etc. Access and queries to this graph data store may thus beaccomplished using an associated access or query language (e.g., such asCypher in the case where the Neo4j graph store is utilized).

Once the identity graph is generated, the graph may then be pruned atstep 230. Here, the identity graph may then be pruned to remove weakedges (e.g., those edges whose similarity weight may fall below apruning threshold). The pruning of the graph is associated with thelocality aspect of identity governance, where an identity's accessentitlements should not be directly impacted, if at all, by anotheridentity with strongly dissimilar entitlement pattern (e.g., a weakconnecting edge). Accordingly, the removal of such edges may notdramatically alter the global topology of the identity graph. An initialpruning threshold may be initially set or determined (e.g., as 50%similarity or the like) and may be substantially optimized or otherwiseadjusted at a later point. As another example, a histogram of similarityweights may be constructed and a similarity weight corresponding to agap in the similarity weights of the histogram may be chosen as aninitial pruning threshold.

The pruned identity graph can then be used to cluster the identitiesinto peer groups of identities at step 240. Within this graph approach,a representation of a peer group could be represented by a maximalclique, where every identity is strongly connected (e.g., similar) toevery other identity within the peer group, and consequently, members ofthe clique all share a relatively large, and hence dominant, common coreof entitlements. The problem of finding all maximal cliques of a graphmay, however, be a memory and computationally intensive problem. Mostclique related problems in graph theory are hard and some of them areeven NP-complete, requiring exponential time to finish as graphs withexponentially many maximal cliques may exist.

Accordingly, in one embodiment a community-detection algorithm may beutilized for peer grouping the identities of the identity graph to speedthe determination of the peer groups, reduce computational overhead andconserve memory, among other advantages. A plethora of applicable andperformant community-detection and graph clustering algorithms may beutilized according to certain embodiments. Some of these algorithms arespecifically targeted to large graphs, which can be loosely described asgraphs with at least tens or hundreds (or more) of thousands of nodesand millions of edges. Such graph community-detection algorithms mayinclude, for example, Louvain, Fast-greedy, Label Propagation orStochastic Block Modeling. Other graph community detection algorithmsmay be utilized and are fully contemplated herein.

In certain embodiments, a clustering result may be optimized through theuse of a feedback loop, as discussed below. As such, in one embodimentit may be desirable to utilize a community-detection algorithm fordetermination of the peer groups that may provide allow astraightforward determination of a peer group assessment metric for aquality assessment of determined peer groups or the identity graph.Accordingly, a community-detection algorithm that may be based on, orallow a determination of, a graph based metric (e.g., modularity,evolving topology, connected components, centrality measures e.g.,betweenness, closeness, community overlap measures (e.g., NMI, Omegaindices)) that may be used as a peer group assessment metric may beutilized.

Specifically, in one embodiment, the Louvain algorithm may be utilizedas a community-detection algorithm and modularity may be used as a peerassessment metric. The Louvain algorithm may not only be a scalablealgorithm that can handle, and be efficient on, large graphs; butadditionally the Louvain algorithm may be based on modularity or bemodularity optimized. Modularity is a scalar that can be determined fora graph or groups or subgraphs thereof. This modularity reflects alikelihood of the clusters generated (e.g., by the algorithm) to nothave been generated by random chance. A high modularity value, (e.g.,positive and away from 0) may indicate that the clustering result isunlikely to be a product of chance. This modularity can thus be used asa peer group assessment metric.

Moreover, In addition to the application of a peer group assessmentmetric to optimize the peer groups or identity graphs determined usingsuch community-detection algorithms, an identity management system mayemploy alerts based these peer group assessment metrics. For example, analert to a user may be based on an alert threshold (e.g., if the peergroup assessment metric drops below or above a certain threshold) or ifany changes over a certain threshold occur with respect to the peergroup assessment metric. For example, setting an empirical low thresholdfor modularity, with combined user alerts, could serve as a warning fordeteriorating quality of peer groups or the identity graph. This couldbe due to input data has been corrupted at some point in pipeline, or inother cases, that the access entitlement process for the particularenterprise is extremely lacking due discipline. Regardless of theunderlying cause, such an early warning system may be valuable to stopthe propagation of questionable data quality in the peer groupassessment and determination process and more generally to identitymanagement goals within the enterprise.

In many cases, the community-detection or other clustering algorithmutilized in an embodiment may fall under the umbrella of what areusually termed unsupervised machine-learning. Results of these types ofunsupervised learning algorithms may leave some room for interpretation,and do not, necessarily or inherently, provide outputs that areoptimized when the domain or context in which they are being applied aretaken into account. Consequently, to mitigate some of these issues andto optimize the use of the peer groups and identity graphs in anidentity governance context, embodiments of identity management systemsemploying such peer groups of identities using an identity graph mayallow some degree of user configuration, where at a least a portion ofthe user configuration may be applied in the graph determination,peer-grouping or optimization of such peer group determination.

This configurability may allow the user of an identity management systemto, for example, impose some constraints or set up certain configurationparameters for the community-detection (or other peer grouping)algorithm in order to enhance the clustering results for a particularuse-case or application. A few non-exhaustive examples of userconfiguration are thus presented. A user may have a strongly definedconcept of what constitutes a ‘peer’. This may entail that the user'sspecification of what continues a peer may be used to derive a pruningthreshold with statistical methods (e.g., rather than relying onmodularity).

As another example of configurability, a user may elect to opt for ahierarchical clustering output, or that peer groups should have certainaverage size, which may entail to allowing for several consecutiveiterations of the community-detection algorithm to be performed (as willbe explained in more detail herein). A user may also elect to run thepeer grouping per certain portions of the identities, versus running itfor all identities. The filtered population of identities may bespecified in terms of geographic location, business role, business unit,etc. Similarly, a user may elect to filter the outputs of thecommunity-detection algorithm in terms of certain identity attributes,e.g., identity role, identity title, identity location, etc. The resultsmight then be quantitatively and qualitatively contrasted againstexisting governance policies to measure, assess and certify compliancewith these policies.

Generally then, a user may elect to utilize the peer grouping feature incombination with other tools of identity governance, in order to gainmore insight into the quality of identity governance policy enforcementwithin the business. This entails that peer grouping should beconfigurable and flexible enough to allow it to be paired with other(e.g., third-party) identity management tools. Accordingly, certainrestrictions may be imposed on the identity graph's or peer group'ssize, format, level of detail, etc.

In any event, once the peer groups of identities pruned identity graphcan then be used to cluster the identities into peer groups ofidentities at step 240 the determined peer groups can then be stored(e.g., separately or in the identity graph itself) and used by theidentity management system. For example, each peer group may be assigneda peer group identifier and the peer group identifier associated witheach identity assigned to the peer group by storing the peer groupidentifier in association with the node in the graph representing thatidentity.

As an example of use a visual representation of the graph, theidentities or the peer groups in the identity graph may be presented toa user of the identity management to assist in compliance orcertification assessments or evaluation of the identities andentitlements as currently used by the enterprise. In principle, strictlyenforced pre-existing governance policies should ensure that identitieswith strongly similar access privileges are strongly similar (e.g., arein the same peer group). The presentation of such peer groups may thus,for example, allow an auditor or compliance assessor to quantitativelyand qualitatively assess the effectiveness of any applicablepre-existing polices, or lack thereof, and how strictly they areenforced.

During such collection, graph determination and peer grouping steps, incertain embodiments, a number of efficiencies may be implemented tospeed the collection process, reduce the amount data that must be storedand to reduce the computer processing overhead and computing cyclesassociated with such data collection, graph determination and peergrouping of such data. Specifically, in one embodiment, a delta changeassessment may be performed when identity management data is collectedor peer groups are determined in a current time period. Morespecifically, if identity management data was collected in a previoustime period, or a previous peer grouping has been performed onidentities of a previously created identity graph, an assessment can bemade (e.g., by a data querying script or process) of the difference (ordelta) between the set of identities corresponding to the most recentprevious snapshot and the set of identities obtained in the current timeperiod. This assessment may comprise a determination of how many changesto the identities, associated entitlements or other attributes haveoccurred between the time of the previous snapshot and the currentsnapshot (e.g., the most recently identity management data collected inthe current time period).

An assessment may also be made of the difference between the peer groupsdetermined from the most recent previous snapshot and the peer groupsobtained in the current time period. This assessment may comprise adetermination of how many identities are associated with different peergroups (e.g., relative to the peer grouping of identities determinedfrom the previous most recent snapshot), changes to the identities orhow many new identities are associated with an established (or new) peergroup.

If there are no determined changes, or the changes are below somethreshold number, or are few, local, or insignificant to a largemajority of existing peer groups, then no action is needed other thanupdating the affected identities in the data of the previous snapshot orthe identity graph. New entries in the entries comprising the currentsnapshot of identities may be created for any newly identifiedidentities. Additionally, nodes in the graph corresponding to newidentities can be appended to an appropriate peer group based on howsimilar this new identity to existing peer groups, (e.g., assign the newidentity the peer group of the same department/title).

If the differences (e.g., number of changes, new identities, differentpeer group assignments, etc.) are non-trivial, affecting a multitude ofidentities across peer groups, then a new peer grouping process mayoccur on the newly refreshed data. In such case, a detection algorithmmay be used to evolve, and persist, previously determined peer groupsinto their recent counterparts. This can be done by monitoring certain‘marker’ identities, e.g., influencers, or identities with highcentrality values and/or high degree of connections, in both versions ofpeer groups. Utilizing a majority vote approach, it can be determinedhow previous peer groups evolve into newer ones. Expected updatedversions of the previous peer group, include splitting, merging, growth,shrinkage. Newer split peer groups may, for example inherit the ‘old’peer group identifiers.

Embodiments of such a delta detection and updating mechanisms may havethe further advantage of allowing the quality and stability of each peergroup to be monitored by an identity management system via tracking thepeer groups or identity graph, the changes thereto, or their evolutionover time. By actively monitoring and assessing the degree of thesechanges between two or more consecutive versions of a peer group oridentity graph, deteriorating quality issues may be detected as theyarise or manifest in the identity graph or peer groups determinedtherefrom. Similarly, using the identity graphs, peer groups or peergroup assessment metrics determined therefrom, a graph evolution modelmay be built in certain embodiments, (e.g., based on epidemiologysusceptible, infected and recovered type models). Comparing the observedevolution of identities, entitlements or peer groups versus theoreticalpredictions may provide another tool to warn users of an identitymanagement system against rapid or extreme changes that may negativelyimpact the quality of peer groups or identity management more generally.

Again, once the peer groups of identities are determined from the prunedidentity graph and stored (at step 240), a peer group assessment metricmay be determined based on the identity graph or the determined peergroups at step 250. As discussed, this peer group assessment metric maybe determined separately based on the peer groups or identity graphdetermined, or may be metric utilized by a community-detectionalgorithm, such that the peer group assessment metric may be determinedas part of the peer group determination process. In certain embodimentsthen, the application of a community-detection algorithm may result insuch a peer group assessment metric (e.g., modularity, evolvingtopology, connected components, centrality measures e.g., betweenness,closeness, community overlap measures (e.g., NMI, Omega indices)) thatmay be used as a peer group assessment metric may be utilized.

For example, as discussed above the Louvain algorithm may be agraph-based modularity optimized community-detection algorithm. Thus, amodularity associated with the determined peer groups may result fromthe determination of the peer group using the Louvain algorithm.Modularity is a scalar that can be determined for a graph or groups orsubgraphs thereof and reflects a likelihood of the clusters generated(e.g., by the algorithm) to not have been generated by random chance. Ahigh modularity value, (e.g., positive and away from 0) may indicatethat the clustering result is unlikely to be a product of chance. Thismodularity can be used as a peer group assessment metric in oneembodiment.

Accordingly, in certain embodiments, the clustering of identities intopeer groups may be optimized based on this peer group assessment metric.Specifically, a feedback loop may be utilized to determine the optimalpruning threshold. The optimization loop may serve to substantiallyincrease or maximize the quality of the graph clustering, with respectto certain proper metrics (e.g., graph modularity or other peer groupassessment metric). Additional domain-specific, per enterprise, criteriamay be utilized in this step in certain embodiments in order to renderclustering results that accurately reflect certain requirements tobetter serve a particular enterprise or use of the per groups oridentity graph.

For instance, in one embodiment if the peer group assessment metric isabove (or above) a quality threshold at step 260 the determination ofpeer groups of identities for the obtained in the current snapshot mayend at step 262. The determined peer groups of identities can then bestored (e.g., separately or in the identity graph) and used by theidentity management system.

However, if the peer group assessment metric is below (or above) aquality threshold at step 260 a feedback loop may be instituted wherebythe pruning threshold is adjusted by some amount at step 270 (up ordown) and the originally determined identity graph is again pruned basedon the adjusted pruning threshold (or the previously pruned identitygraph may be further pruned) at step 230. The adjustment of the pruningthreshold may be based on a wide variety of criteria in variousembodiments and may be adjust be a fixed or differing amount in everyiteration through the feedback loop. Additionally, in some embodiments,various machine learning techniques (e.g., unsupervised machine learningtechniques such as k-means, method of moments, neural networks, etc.)may be used to determine an amount to adjust the pruning threshold or avalue for the adjusted pruning threshold). This newly pruned identitygraph can then be clustered into new peer groups of identities at step240 and a peer group assessment metric determined at step 250 based onthe newly pruned identity graph or the newly determined peer groups.

If this new peer assessment metric is now above (or below) the qualitythreshold at step 260 the feedback loop may be stopped and thedetermination of peer groups of identities for the obtained in thecurrent snapshot may end at step 262. These peer groups of identitiescan then be stored (e.g., separately or in the identity graph) and usedby the identity management system.

Otherwise, the feedback loop may continue by again adjusting the pruningthreshold further at step 270 (e.g., further up or further down relativeto the previous iteration of the feedback loop), re-pruning the identitygraph based on the adjusted pruning threshold at step 230, clusteringthis newly pruned graph at step 240, determining another peer groupassessment metric at step 250 and comparing this metric to the qualitythreshold at step 260. In this manner, the feedback loop of adjustmentof the pruning threshold, re-pruning the graph, re-clustering theidentity graph into peer groups may be repeated until the peer groupassessment metric reaches a desired threshold. Moreover, by tailoringthe peer group assessment metric and quality threshold to include orreflect domain or enterprise specific criteria (e.g., which may bespecified by a user of the identity management system), the clusteringresults (e.g., the peer groups resulting from the clustering) may moreaccurately reflect particular requirements or the needs of a particularenterprise or be better tailored to a particular use.

Once the feedback loop is ended (step 262) the determined peer groups ofidentities can then be stored (e.g., separately or in the identitygraph) and used by the identity management system. For example, a visualrepresentation of the graph may be presented to a user of the identitymanagement to assist in compliance or certification assessments orevaluation of the identities and entitlements as currently used by theenterprise.

It may now be helpful to look at such visual depictions andpresentations of identity graphs or interfaces that may be created orpresented based on such identity graphs. It will be apparent that thesedepictions and interfaces are but example of depictions and interfacesthat may presented or utilized, and that almost any type ofpresentation, depiction or interface based on the identities,entitlements, peer groups other associated data discussed may beutilized in association with the embodiments of identity managementsystems disclosed herein.

As discussed embodiments of the identity management systems as disclosedmay create, maintain or utilize identity graphs. These identity graphsmay include a graph comprised of nodes and edges, where the nodes mayinclude identity management nodes representing, for example, anidentity, entitlement or peer group, and the edges may includerelationships between these identity management nodes. The relationshipsrepresented by the edges of the identity graph may be assigned weightsor scores indicating a degree of similarity between the nodes related bya relationship, including, for example, the similarity between two nodesrepresenting an identity as discussed. Additionally, the relationshipsmay be directional, such that they may be traversed only in a singledirection, or have different weightings depending on the direction inwhich the relationship is traversed or the nodes related. Embodiments ofsuch an identity graph can thus be searched (or navigated) to determinedata associated with one or more nodes. Moreover, the similaritybetween, for example, the identities may be determined using the weightsof the relationships in the identity graph.

Specifically, in certain embodiments, an identity graph may be thoughtof as a graph comprising a number of interrelated nodes. These nodes mayinclude nodes that may have labels defining the type of the node (e.g.,the type of “thing” or entity that the node represents, such as anidentity, entitlement or peer group) and properties that define theattributes or data of that node. For example, the labels of the nodes ofan identity graph may include “Identity”, “Entitlement” or “PeerGroup”.Properties of a node may include, “id”, “company”, “dept”, “title”,“location”, “source” “size”, “clique”, “mean_similarty”, or the like.

The nodes of the identity graph may be interrelated using relationshipsthat form the edges of the graph. A relationship may connect two nodesin a directional manner. These relationships may also have a label thatdefines the type of relationship and properties that define theattributes or data of that relationship. These properties may include anidentification of the nodes related by the relationship, anidentification of the directionality of the relationship or a weight ordegree of affinity for the relationship between the two nodes. Forexample, the labels of the relationships of an identity graph mayinclude “Similarity” or “SIM”, “Has_Entitlement” or “HAS_ENT”,“Belongs_To_PeerGroup” or “BELONGS_TO_PG”, or the like.

Referring then to FIG. 3A, a graphical depiction of a portion of anexample identity graph 300 is depicted. Here, nodes are represented bycircles and relationships are represented by the directional arrowsbetween the nodes. Such an identity graph 300 may represent identities,entitlements or peer groups, their association, and the degree ofsimilarity between identities represented by the nodes. Thus, forexample, the identity nodes 302 a, 302 b have the label “Identity”indicating they are identity nodes. Identity node 302 b is shown asbeing associated with a set of properties that define the attributes ordata of that identity node 302 b, including here that the “id” ofidentity node 302 b is “a123”, the “company” of identity node 302 b is“Ajax”, the “dept” of identity node 302 b is “Sales”, the “title” ofidentity node 302 b is “Manager”, and the “location” of identity node302 b is “Austin, Tex.”.

These identity nodes 302 of the identity graph 300 are joined by edgesformed by directed relationships 312 a, 312 b. Directed relationship 312a may represent that the identity of identity node 302 a is similar to(represented by the labeled “SIM” relationship 312 a) the identityrepresented by identity node 302 b. Similarly, directed relationship 312b may represent that the identity of identity node 302 b is similar to(represented by the labeled “SIM” relationship 312 b) the identityrepresented by identity node 302 a. Here, relationship 312 b has beenassigned a similarity weight of 0.79. Notice that while theserelationships 312 a, 312 b are depicted as individual directionalrelationships, such a similar relationship may be a single bidirectionalrelationship assigned a single similarity weight.

Entitlement node 304 has the label “Entitlement” indicating that it isan entitlement node. Entitlement node 304 is shown as being associatedwith a set of properties that define the attributes or data of thatentitlement node 304, including here that the “id” of entitlement node304 is “ad137”, and the “source” of entitlement node 304 is “ActiveDirectory”. Identity node 302 b and entitlement node 304 of the identitygraph 300 are joined by an edge formed by directed relationship 316.Directed relationship 316 may represent that the identity of identitynode 302 b has (represented by the labeled “HAS_ENT” relationship 316)the entitlement represented by entitlement node 304.

Peer group node 306 has the label “PeerGroup” indicating that it is apeer group node. Peer group node 306 is shown as being associated with aset of properties that define the attributes or data of that peer groupnode 306, including here that the “id” of peer group node 306 is“pg314”, the “size” of peer group node 306 is “287”, the “clique” ofpeer group node 306 is “0.83” and the “mean_sim” or mean similarityvalue of peer group node 306 is “0.78”. Identity node 302 b and peergroup node 306 of the identity graph 300 are joined by an edge formed bydirected relationship 314. Directed relationship 314 may represent thatthe identity of identity node 302 b belongs to (represented by thelabeled “BELONGS_TO_PG” relationship 314) the peer group represented byentitlement node 304.

Now referring to FIGS. 3B, 3C and 3D, example representations of peergroupings within identity graphs are depicted. Here, each identity nodeof an identity graph is represented by a circle and each edge isrepresented by a line joining the nodes. In these visual depictions, thecloser the nodes the higher the similarity value between the nodes. Suchvisual depictions when presented to a user may allow a user to betterperceive the number of identities utilized by an enterprise, therelationships between those identities, the distribution of entitlementswith respect to those identities or other information related to theidentities or entitlements that may be utilized in identity governanceand management, including for example, compliance assessment orauditing.

FIG. 4 depicts an embodiment of an interface that may be utilized by anidentity management system to visually present data regarding the peergroups determined for identities within an enterprise. In this example,the enterprise has 9235 associated identities, and the interface depictsthat there are 6 peer groups of those identities that have beendetermined based on the entitlements associated with the identities.Each of the depicted circles 410 within the interface represents one ofthe peer groups and displays the number of identities associated witheach of those peer groups. Moreover, the size and location of eachcircle 410 may depict the relative size of the peer groups of theidentities and the number of entitlements shared between those peergroups, or identities within those peer groups.

FIG. 5 depicts an embodiment of interface that may be utilized by anidentity management system to visually present data regarding the peergroups determined for identities within an enterprise. Here, theinterface may present a visual representation of the identity graph asdiscussed above where each identity node is represented by a circle andeach edge is represented by a line joining the nodes, where the closerthe nodes the higher the similarity value between the nodes. Theinterface may also present information regarding the number of peergroups (clusters) determined for the identity graph being presented (inthis example 11).

The interface, or a portion thereof, may allow the user to navigatearound the identity graph and “drill down” to obtain information on arepresented node or entitlement. In the depicted example, the user hashovered above a node 510 of the identity graph and information aboutthat identity is presented through the interface to the user. By lookingat such an identity graph a user may be able to discern, for example,which identities which may be “highly contagious” or represent otheridentity management risks or compliance issues. An identity may be“highly contagious” or otherwise represent an identity governance risk,for example, if that identity may have a number or type of entitlementsuch that if those identities are replicated without identity governanceoversight (e.g., assigned to other users) it may cause identitygovernance issues such as unintended entitlement bloom.

FIG. 6 depicts an embodiment of another interface that may be utilizedby an identity management system to visually present data regarding thepeer groups determined for identities within an enterprise. In thisexample, the interface can present data regarding a particular peergroup determined for an identity graph, showing, for example, the numberof identities within that peer group, what the entitlements are withinthat peer group, what identities share those entitlements, or why thoseidentities have been grouped together. The interface may also present awide variety of other data regarding that peer group or identities orentitlements within that (or other) peer groups, including for example,how that peer group, identities within that peer group or otherentitlements relate to each other or other determined peer groups,identities or entitlements of the enterprise. Thus, a user viewing suchan interface may be able to ascertain reasons why the identities havebeen grouped and explore for outliers and see entitlements that theseidentities have in common with each other, as well as how different theyare from the rest of the identities and entitlements of an enterprise.Moreover, the user may also “drill down” for more details to discoverwhich identities included and the entitlements assigned.

FIG. 7 depicts an embodiment of still another interface that may beutilized by an identity management system to visually present dataregarding the peer groups determined for identities within anenterprise. In this example, the interface can present data regarding aparticular peer group (e.g., peer group 43) determined for an identitygraph, showing, for example, distributions of identities within the peergroup, such as the identities of the peer group's correlation withdepartments, location or job title.

Those skilled in the relevant art will appreciate that the invention canbe implemented or practiced with other computer system configurationsincluding, without limitation, multi-processor systems, network devices,mini-computers, mainframe computers, data processors, and the like.Embodiments can be employed in distributed computing environments, wheretasks or modules are performed by remote processing devices, which arelinked through a communications network such as a LAN, WAN, and/or theInternet. In a distributed computing environment, program modules orsubroutines may be located in both local and remote memory storagedevices. These program modules or subroutines may, for example, bestored or distributed on computer-readable media, including magnetic andoptically readable and removable computer discs, stored as firmware inchips, as well as distributed electronically over the Internet or overother networks (including wireless networks). Example chips may includeElectrically Erasable Programmable Read-Only Memory (EEPROM) chips.Embodiments discussed herein can be implemented in suitable instructionsthat may reside on a non-transitory computer readable medium, hardwarecircuitry or the like, or any combination and that may be translatableby one or more server machines. Examples of a non-transitory computerreadable medium are provided below in this disclosure.

Although the invention has been described with respect to specificembodiments thereof, these embodiments are merely illustrative, and notrestrictive of the invention. Rather, the description is intended todescribe illustrative embodiments, features and functions in order toprovide a person of ordinary skill in the art context to understand theinvention without limiting the invention to any particularly describedembodiment, feature or function, including any such embodiment featureor function described. While specific embodiments of, and examples for,the invention are described herein for illustrative purposes only,various equivalent modifications are possible within the spirit andscope of the invention, as those skilled in the relevant art willrecognize and appreciate.

As indicated, these modifications may be made to the invention in lightof the foregoing description of illustrated embodiments of the inventionand are to be included within the spirit and scope of the invention.Thus, while the invention has been described herein with reference toparticular embodiments thereof, a latitude of modification, variouschanges and substitutions are intended in the foregoing disclosures, andit will be appreciated that in some instances some features ofembodiments of the invention will be employed without a correspondinguse of other features without departing from the scope and spirit of theinvention as set forth. Therefore, many modifications may be made toadapt a particular situation or material to the essential scope andspirit of the invention.

Reference throughout this specification to “one embodiment”, “anembodiment”, or “a specific embodiment” or similar terminology meansthat a particular feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodimentand may not necessarily be present in all embodiments. Thus, respectiveappearances of the phrases “in one embodiment”, “in an embodiment”, or“in a specific embodiment” or similar terminology in various placesthroughout this specification are not necessarily referring to the sameembodiment. Furthermore, the particular features, structures, orcharacteristics of any particular embodiment may be combined in anysuitable manner with one or more other embodiments. It is to beunderstood that other variations and modifications of the embodimentsdescribed and illustrated herein are possible in light of the teachingsherein and are to be considered as part of the spirit and scope of theinvention.

In the description herein, numerous specific details are provided, suchas examples of components and/or methods, to provide a thoroughunderstanding of embodiments of the invention. One skilled in therelevant art will recognize, however, that an embodiment may be able tobe practiced without one or more of the specific details, or with otherapparatus, systems, assemblies, methods, components, materials, parts,and/or the like. In other instances, well-known structures, components,systems, materials, or operations are not specifically shown ordescribed in detail to avoid obscuring aspects of embodiments of theinvention. While the invention may be illustrated by using a particularembodiment, this is not and does not limit the invention to anyparticular embodiment and a person of ordinary skill in the art willrecognize that additional embodiments are readily understandable and area part of this invention.

Embodiments discussed herein can be implemented in a set of distributedcomputers communicatively coupled to a network (for example, theInternet). Any suitable programming language can be used to implementthe routines, methods or programs of embodiments of the inventiondescribed herein, including R, Python, C, C++, Java, JavaScript, HTML,or any other programming or scripting code, etc. Othersoftware/hardware/network architectures may be used. Communicationsbetween computers implementing embodiments can be accomplished using anyelectronic, optical, radio frequency signals, or other suitable methodsand tools of communication in compliance with known network protocols.

Although the steps, operations, or computations may be presented in aspecific order, this order may be changed in different embodiments. Insome embodiments, to the extent multiple steps are shown as sequentialin this specification, some combination of such steps in alternativeembodiments may be performed at the same time. The sequence ofoperations described herein can be interrupted, suspended, or otherwisecontrolled by another process, such as an operating system, kernel, etc.The routines can operate in an operating system environment or asstand-alone routines. Functions, routines, methods, steps and operationsdescribed herein can be performed in hardware, software, firmware or anycombination thereof.

Embodiments described herein can be implemented in the form of controllogic in software or hardware or a combination of both. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code). Examples of non-transitory computer-readable mediacan include random access memories, read-only memories, hard drives,data cartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein, a termpreceded by “a” or “an” (and “the” when antecedent basis is “a” or “an”)includes both singular and plural of such term, unless clearly indicatedwithin the claim otherwise (i.e., that the reference “a” or “an” clearlyindicates only the singular or only the plural). Also, as used in thedescription herein and throughout the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

What is claimed is:
 1. An identity management system, comprising: amemory; a processor; a non-transitory, computer-readable storage mediumincluding computer instructions for: presenting an identity managementinterface, including: presenting identity management information throughthe identity management interface, wherein the identity managementinformation is determined from a representation of identity managementdata utilized in identity management in a distributed enterprisecomputing environment and comprising data on a set of identities and aset of entitlements associated with the set of identities, wherein therepresentation of the identity management data includes: arepresentation for each of the set of identities, a representation of arelationship between a first identity and a second identity for eachfirst identity and second identity of the identity management data thatshare at least one entitlement of the set of entitlements, wherein therelationship has an associated weight based on the at least one sharedentitlement between the first identity and the second identity, and arepresentation of a peer group of identities of the set of identities,the peer group determined based on a clustering of the set of identitiesbased on the relationships in the representation of the identitymanagement data.
 2. The identity management system of claim 1, whereinthe presented identity management information comprises information onthe peer group.
 3. The identity management system of claim 1, whereinthe representation of the identity management data is an identity graph.4. The identity management system of claim 3, wherein the identity graphis stored in a graph database.
 5. The identity management system ofclaim 3, wherein the presented identity management information comprisesa depiction of the identity graph.
 6. The identity management system ofclaim 1, wherein the presented identity management information comprisesinformation on an identity management risk.
 7. A method, comprising:presenting an identity management interface, including: presentingidentity management information through the identity managementinterface, wherein the identity management information is determinedfrom a representation of identity management data utilized in identitymanagement in a distributed enterprise computing environment andcomprising data on a set of identities and a set of entitlementsassociated with the set of identities, wherein the representation of theidentity management data includes: a representation for each of the setof identities, a representation of a relationship between a firstidentity and a second identity for each first identity and secondidentity of the identity management data that share at least oneentitlement of the set of entitlements, wherein the relationship has anassociated weight based on the at least one shared entitlement betweenthe first identity and the second identity, and a representation of apeer group of identities of the set of identities, the peer groupdetermined based on a clustering of the set of identities based on therelationships in the representation of the identity management data. 8.The method of claim 7, wherein the presented identity managementinformation comprises information on the peer group.
 9. The method ofclaim 7, wherein the representation of the identity management data isan identity graph.
 10. The method of claim 9, wherein the identity graphis stored in a graph database.
 11. The method of claim 9, wherein thepresented identity management information comprises a depiction of theidentity graph.
 12. The method of claim 7, wherein the presentedidentity management information comprises information on an identitymanagement risk.
 13. A non-transitory computer readable medium,comprising instructions for: presenting an identity managementinterface, including: presenting identity management information throughthe identity management interface, wherein the identity managementinformation is determined from a representation of identity managementdata utilized in identity management in a distributed enterprisecomputing environment and comprising data on a set of identities and aset of entitlements associated with the set of identities, wherein therepresentation of the identity management data includes: arepresentation for each of the set of identities, a representation of arelationship between a first identity and a second identity for eachfirst identity and second identity of the identity management data thatshare at least one entitlement of the set of entitlements, wherein therelationship has an associated weight based on the at least one sharedentitlement between the first identity and the second identity, and arepresentation of a peer group of identities of the set of identities,the peer group determined based on a clustering of the set of identitiesbased on the relationships in the representation of the identitymanagement data.
 14. The non-transitory computer readable medium ofclaim 13, wherein the presented identity management informationcomprises information on the peer group.
 15. The non-transitory computerreadable medium of claim 13, wherein the representation of the identitymanagement data is an identity graph.
 16. The non-transitory computerreadable medium of claim 15, wherein the identity graph is stored in agraph database.
 17. The non-transitory computer readable medium of claim15, wherein the presented identity management information comprises adepiction of the identity graph.
 18. The non-transitory computerreadable medium of claim 13, wherein the presented identity managementinformation comprises information on an identity management risk.